Design auto drift detection and retraining
WHAT IT TESTS: closed-loop MLOps design. OUTLINE: capture inputs and predictions, compute data and concept drift metrics on a schedule, alert on threshold breach, and trigger a retraining and redeploy pipeline.
WHAT IT TESTS: whether you can design a self-correcting model system. ANSWER OUTLINE: log every inference input and prediction; a scheduled job compares the live feature and label distributions against a training baseline using metrics such as population stability index, KL divergence, or a Kolmogorov-Smirnov test for data drift, plus accuracy decay once ground truth arrives for concept drift; breaching a threshold fires an alert and triggers a retraining pipeline that validates and canaries the new model.
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- #drift-detection
- #mlops
- #monitoring
- #retraining
- #cloud
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